What data and related contextual problems are appropriate for designing learning opportunities related to AI? What do students need to know about the concept of data to do data science and understand AI? What do students need to understand about data as constructed, contextual, and value-laden to meaningfully engage with AI systems?

Which (digital) tools and infrastructures are suitable or adaptable for teaching, learning, and doing data science and AI at school level? How do tools enable or constrain understanding, agency, and critical reflection?

What kinds of educational and explanatory models, at which levels of abstraction, are suitable for different students? Where are black boxes pedagogically productive, and where do they obscure epistemic limits, uncertainty, or power relations?

How can teaching and learning materials be designed to balance simplification, authenticity, and critical depth? Which concepts can be elementarised? What are good practice examples?

What key competencies should responsible citizens acquire in the field of AI and data science? Which AI and data science competencies should already be promoted at school? What contribution can and must different subjects make? How can relevant competencies be assessed?

How can AI education be integrated in schools and teacher education? How can AI and data science education be integrated into existing subject curricula (e.g. computer science, mathematics, social and natural sciences)? What could an (interdisciplinary) AI and data science curriculum look like?

How can AI and data science education (e.g. learning environment, selection of data, etc.) be designed to effectively address social issues and promote societal well-being? What ethical considerations should guide our understanding of AI and data science in teaching?